Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes.

IF 1.8 Q3 UROLOGY & NEPHROLOGY Advances in Urology Pub Date : 2019-04-23 eCollection Date: 2019-01-01 DOI:10.1155/2019/3590623
Rahul Rajendran, Kevan Iffrig, Deepak K Pruthi, Allison Wheeler, Brian Neuman, Dharam Kaushik, Ahmed M Mansour, Karen Panetta, Sos Agaian, Michael A Liss
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引用次数: 2

Abstract

Objective: To develop software to assess the potential aggressiveness of an incidentally detected renal mass using images.

Methods: Thirty randomly selected patients who underwent nephrectomy for renal cell carcinoma (RCC) had their images independently reviewed by engineers. Tumor "Roughness" was based on image algorithm of tumor topographic features visualized on computed tomography (CT) scans. Univariant and multivariant statistical analyses are utilized for analysis.

Results: We investigated 30 subjects that underwent partial or radical nephrectomy. After excluding poor image-rendered images, 27 patients remained (benign cyst = 1, oncocytoma = 2, clear cell RCC = 15, papillary RCC = 7, and chromophobe RCC = 2). The mean roughness score for each mass is 1.18, 1.16, 1.27, 1.52, and 1.56 units, respectively (p < 0.004). Renal masses were correlated with tumor roughness (Pearson's, p=0.02). However, tumor size itself was larger in benign tumors (p=0.1). Linear regression analysis noted that the roughness score is the most influential on the model with all other demographics being equal including tumor size (p=0.003).

Conclusion: Using basic CT imaging software, tumor topography ("roughness") can be quantified and correlated with histologies such as RCC subtype and could lead to determining aggressiveness of small renal masses.

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计算机辅助放射学评估对肾肿块边缘检测作为肿瘤粗糙度指标预测肾癌亚型的初步评价。
目的:开发一种软件来评估偶然发现的肾脏肿块的潜在侵袭性。方法:随机选择30例接受肾细胞癌(RCC)切除术的患者,由工程师独立审查其图像。肿瘤“粗糙度”是基于计算机断层扫描(CT)上可视化的肿瘤地形特征的图像算法。采用单变量和多变量统计分析进行分析。结果:我们调查了30例接受部分或根治性肾切除术的患者。排除图像渲染不良的图像后,剩余27例患者(良性囊肿1例,嗜瘤细胞瘤2例,透明细胞RCC 15例,乳头状RCC 7例,憎色RCC 2例)。每个质量的平均粗糙度评分分别为1.18、1.16、1.27、1.52和1.56个单位(p < 0.004)。肾肿块与肿瘤粗糙度相关(Pearson’s, p=0.02)。而良性肿瘤本身的肿瘤大小更大(p=0.1)。线性回归分析指出,在包括肿瘤大小在内的所有其他人口统计数据相等的情况下,粗糙度评分对模型的影响最大(p=0.003)。结论:使用基本的CT成像软件,可以量化肿瘤的地形(“粗糙度”),并与RCC亚型等组织学相关联,从而确定肾小肿块的侵袭性。
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来源期刊
Advances in Urology
Advances in Urology UROLOGY & NEPHROLOGY-
CiteScore
2.90
自引率
0.00%
发文量
17
审稿时长
15 weeks
期刊介绍: Advances in Urology is a peer-reviewed, open access journal that publishes state-of-the-art reviews and original research papers of wide interest in all fields of urology. The journal strives to provide publication of important manuscripts to the widest possible audience worldwide, without the constraints of expensive, hard-to-access, traditional bound journals. Advances in Urology is designed to improve publication access of both well-established urologic scientists and less well-established writers, by allowing interested scientists worldwide to participate fully.
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